Portfolio
Customer Segmentation Analysis for Clifford's eCommerce Website
Analyzing Health & Environmental Factors Impact on Wellbeing in US
Shopify Super Store Profit Report
Sarah-Tees Sales-Analysis Region Dynamics & Strategic Insights
Utilizing K-means clustering, the project segmented visitors into 4 distinct groups based on transaction behavior and browsing patterns. Identified clusters include Loyal Shoppers, Bouncers, Deal Seekers, and Confused Shoppers. Recommendations encompass implementing a loyalty program, targeted promotions for non-buying segments, website redesign focusing on user experience, and personalized reminders to enhance engagement and conversion rates. Regular updates to the ML model are advised for ongoing accuracy and effectiveness.
Using BRFSS and county-level data, this study examines health and environmental factors influencing US residents' well-being. Exploratory analysis unveils demographic, health behavior, and socioeconomic patterns, while predictive analytics via logistic regression and ML models identify social support, mental health, and employment status as crucial predictors. Recommendations aim to enhance public health by fostering social networks, mental health awareness, and addressing socioeconomic disparities.
The project focuses on leveraging sales data analysis via Power BI, using SQL and DAX query languages for data cleaning and dashboard creation. It aims to explore past, present, and future sales trends, product performance across regions, and overall market insights. Through descriptive research, the project emphasizes the application of business analytics to shape effective marketing strategies, presenting a large dataset visually for informed business decisions and enhanced performance assessment.
Regional customer bases in CA EAST and US East dominate Sarah Tees' presence in Canada and the US, respectively. Sales discrepancies favor the US in 2023, despite a larger Canadian customer base in Canada East. Resellers significantly impact higher sales in US East and US WEST regions, contributing to smaller customer base revenue. Actionable strategies focus on addressing sales drops via surveys, targeting Resellers and Corporate accounts to regain customer interest and boost sales
Nifty Bank Stock Analysis using VWAP
Utilizing Python's Pandas, NumPy, and Matplotlib libraries, our project focuses on conducting a comprehensive analysis of Nifty Bank stocks by implementing Volume Weighted Average Price (VWAP). Through data collection and manipulation using Pandas, we'll calculate VWAP for key constituents such as HDFC Bank Ltd., ICICI Bank Ltd., and Axis Bank Ltd. Employing NumPy for numerical computations and Matplotlib for visualizations, this analytical approach will uncover crucial insights into the market behavior and trends of these leading Indian banking stocks. This analysis serves as a valuable tool for investors, enabling informed decision-making based on robust data-driven conclusions.
Automobile Feature Analysis and Price Prediction
Our project employs Python's Pandas, NumPy, and Scikit-learn for analyzing automotive data, predicting prices based on key features. Through exploratory data analysis (EDA) and visualization using Matplotlib and Seaborn, we'll uncover correlations between car attributes and prices. Employing machine learning algorithms like regression and decision trees, we aim to build predictive models to estimate car prices accurately. This project's goal is to offer a robust predictive framework for car pricing, leveraging data analytics and Python's capabilities, benefiting both buyers and sellers in the automotive market with informed decision-making tools.
Analysis of Immigration to Canada
Stock Analysis via Web Scraping
Our Python-driven project uses Pandas, NumPy, and Scikit-learn for in-depth analysis of Canadian immigration data. Through exploratory data analysis (EDA) and visualization with Matplotlib or Seaborn, we'll uncover migration trends, demographics, and origin information. Utilizing advanced analytics and visualization techniques, we aim to showcase insightful representations of immigration patterns. Additionally, by implementing machine learning algorithms like clustering or predictive models, we strive to reveal hidden insights and forecast potential future migration trends. This project aims to provide a comprehensive understanding of Canadian immigration, empowering policymakers and stakeholders with data-driven insights for informed decision-making and strategic planning.
Our Python-based project utilizes BeautifulSoup and requests for web scraping financial data from diverse stock market websites. Using Pandas for data manipulation and Matplotlib for visualization, we aim to process and analyze the scraped data, extracting crucial metrics like stock prices, volumes, and market trends. Through efficient web scraping techniques, we'll gather real-time data, enabling the creation of comprehensive visualizations and detailed stock market analyses. Leveraging Python's analytical capabilities, our project intends to offer robust insights, encompassing trend analysis, volatility assessment, and correlation studies, providing investors with actionable information for well-informed decision-making in the dynamic stock market landscape.
